Results 171 to 180 of about 2,349 (245)

Hybrid Physics-Informed Residual Learning for Robust BDS-3 Satellite Clock Bias Prediction. [PDF]

Sensors (Basel)
Cheng L, Li K, Guan W, Li Z, Liang Q, Cai C.   +5 more
europepmc   +1 more source

Seamless Indoor and Outdoor Navigation Using IMU-GNSS Sensor Data Fusion. [PDF]

Sensors (Basel)
Asiedu Asante BK, Imamura H.
europepmc   +1 more source

Physics-Informed Neural Network Based Digital Image Correlation Method

Digital Image Correlation (DIC) is a key technique in experimental mechanics for full-field deformation measurement, traditionally relying on subset matching to determine displacement fields. However, selecting optimal parameters like shape functions and
Li, Boda, Ma, Qinwei, Ma, Shaopeng, Zhou, Shichao   +3 more
core  

A Physics-Informed Neural Network Framework Integrating Soft and Hard Constraints for Predicting Biomass Gasification Syngas Compositions. [PDF]

ACS Omega
Zou Q, Huang H, Liu X, Ding J, Ren S, Si F.   +5 more
europepmc   +1 more source

Physics Informed Neural Networks (PINNs) for neutronic equations

Artificiell Intelligens (AI), och mer specifikt Physics-Informed Neural Networks (PINNs), spelar en alltmer central roll inom moderna vetenskapliga och industriella tillämpningar och driver innovation inom en rad olika områden. Denna avhandling, "Physics Informed Neural Networks (PINNs) för neutroniska ekvationer", utforskar potentialen hos AI-baserade
openaire   +1 more source

Physics-informed deep learning for molecular solubility prediction: integrating thermodynamic constraints with neural network architectures. [PDF]

Sci Rep
Amiri M.
europepmc   +1 more source

Peristaltic transport and thermodynamic analysis of hybrid nanofluids in porous media using physics-informed neural networks. [PDF]

Discov Nano
Vaseem M, Uddin Z, Upreti H.
europepmc   +1 more source

Densely Multiplied Physics Informed Neural Networks

Although physics-informed neural networks (PINNs) have shown great potential in dealing with nonlinear partial differential equations (PDEs), it is common that PINNs will suffer from the problem of insufficient precision or obtaining incorrect outcomes ...
Xia, Min, Jiang, Feilong, Hou, Xiaonan
core  

A Nonlinear Error Compensation Method for Heterodyne Interferometry Based on Self-Supervised Physics-Informed Neural Networks with Frequency-Domain Priors. [PDF]

Sensors (Basel)
Wang Y, Sun H, Li J, Ma C, Zhang Y, Wang X, Feng Q.   +6 more
europepmc   +1 more source

Physics Informed Neural Network Framework for Unsteady Discretized Reduced Order System

This work addresses the development of a physics-informed neural network (PINN) with a loss term derived from a discretized time-dependent reduced-order system. In this work, first, the governing equations are discretized using a finite difference scheme
Stabile, Giovanni, Halder, Rahul, Rozza, Gianluigi   +2 more
core